A natural language processing model may be a machine learning system, or component thereof, used by a computer system to interact with human languages. For example, a natural language processing model may receive a query as input, and may make predictions about the text of the query to help determine what the query is requesting and what information or actions might be relevant responses to the query.
Natural language processing models may be trained using training examples from well-behaved domains. For example, news reports that have been human-annotated with part-of-speech tagging may be training examples used to train a natural language processing model. When a natural language processing model that has been trained on training examples from a well-behaved domain is given input such as search queries and potential search results, which may be, for example, web documents, the results may be much worse than when the natural language processing model is given input similar to the training examples. Search queries may be short, grammatically unsound, and lacking in context. The natural language processing model may have difficulty identifying the part-of-speech of words in a search query, including disambiguating syntactically confusable labels, and determining the syntactic structure of the query. This may reduce the usefulness of natural language processing models in interpreting web documents and responding to search queries. There may be few training examples available with which to train the natural language processing model to interpret search queries.